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dc.contributor.authorStoy, Paul C.
dc.contributor.authorQuaife, Tristan
dc.date.accessioned2015-11-18T19:11:12Z
dc.date.available2015-11-18T19:11:12Z
dc.date.issued2015-06
dc.identifier.citationStoy, Paul C., and Tristan Quaife. "Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling." PLoS ONE 10, no. 6 (June 2015): e0128935. DOI:https://dx.doi.org/10.1371/journal.pone.0128935.en_US
dc.identifier.issn1932-6203
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/9382
dc.description.abstractUpscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.en_US
dc.description.sponsorshipNSF DBI #1021095; NSF EF #1241881; Marie Curie Incoming International Fellowship programme;Montana State University; Natural Environment Research Council, grant number ARSF 03/17; USDA National Institute of Food and Agriculture, Hatch project 228396en_US
dc.rightsCC BY 4.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleProbabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modelingen_US
dc.typeArticleen_US
mus.citation.extentfirstpagee0128935en_US
mus.citation.issue6en_US
mus.citation.journaltitlePLoS ONEen_US
mus.citation.volume10en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1371/journal.pone.0128935en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage7en_US


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